Deep Convolution Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve the problems of character recognition, but their use has become as widespread as it is now thanks to recent research. These CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolution neural network became a very useful tool in the field of the machine learning.
The CNNs are sometimes used in a field of autonomous vehicle. That is, the vehicle can be safely driven via a function of detecting obstacles, free spaces, and the like by analyzing images obtained with a camera attached to the vehicle.
It is security of the CNNs for the autonomous driving that should be considered to be very important when the CNNs are actually used for the autonomous driving. As the autonomous vehicle with heavy weight moves at high speeds, the risk is high in case of an erroneous driving. Specifically, if a hacker with a bad intention falsifies parameters of the CNNs for the autonomous driving by hacking the CNNs, the autonomous vehicle may be used for terrorism.
Therefore, it is necessary to verify whether the parameters of the CNN during a test process are same as those of the CNN at the time when the CNN has completed its learning process. Researches so far have been focused mainly on how many effects the CNNs can have on driving the vehicle, and there is little research on how to maintain the security.
Further, even if a surveillance system maintains the security of the CNN by using conventional technologies in other fields, there are disadvantages that a main function of the CNN may deteriorate due to a lot of additional operations besides operations of the CNN.